1. Field of the Invention
The present invention relates to a computer system, and more particularly to a computer system and method for generating a plurality of artifacts in a specified language which people (e.g., humans) are likely to find interesting.
2. Description of the Related Art
The notion of general creativity, like intelligence, is ultimately a humanistic and subjective concept. Moreover, the notion of computational creativity is not bound to the same procedural attributes one might associate with human creativity.
Indeed, the procedural attributes of human creativity may remain largely a mystery, while the procedural attributes of computational creativity may be well-defined. This comparison is akin to the comparison between the human intelligence at work playing a game of chess and the computational mechanism at work in a computer that rivals the human player. Both mechanisms manifest an effective performance in, what humans consider, an intellectually demanding task. However, the procedural attributes of the human remain largely ill-defined, while the procedural mechanisms used by the computer may be rigorously explicated in a formal computational language and ultimately vary significantly from the human mechanism.
Therefore, the association of creativity with an agent is focused on manifest behavior, not on implementation. One ultimately judges an agent as “creative” if it can produce an interesting artifact in some language of expression (e.g., natural language, music, sculpture, etc.), starting from a point sufficiently distant from the end result.
Two key concepts in realizing a creative agent are the notions of interestingness and creative distance. “Interestingness” asks the question of: “will one find an agent's creation interesting or will one find it an incomprehensible blob with no communicative value?”
An agent may generate voluminous works that are never assigned any value by the agent's audience. The agent, rather than being judged creative, is considered an arbitrary generator of random artifacts. There are several ways to consider interestingness.
First, the agent may understand the cognitive cultural context of its audience well enough to invent the interesting. That is, the agent may extend and redefine the audience's cognitive cultural awareness with a novel creation and demonstrate its value. One typically associates genius with such capability. Computational creativity does not address genius.
A second way to create interesting artifacts is to begin with a known seed of interestingness (e.g., what is referred to as a “theme”). For example, it is known that particular stories about the fruits or snarls of romantic love have human interest. Variations based on skillful use of language, plot twists. rich characterizations etc. that appeal to audiences are considered creative and interesting, albeit perhaps short of genius.
While the output of a generative agent may be an interesting well-crafted story, a harmonious piece of music or a delicious recipe, the creativity of the agent is ultimately predicated on the input's creative distance from the output.
For example, if an agent for story generation requires a complete story as an input and outputs different stories differing from the input only by the names of the characters, then the agent, while legitimately generating a unique artifact, would not be considered creative. Indeed, the “distance” between the input and the output would be considered negligible and insufficient. Nor would the artifact produced by the agent effect significant human interest in light of the input.
However, if a literary theme such as the “evil of betrayal” or the “destructive force of ambition” were the input to the agent, and the agent output complete stories about the indicated theme varying according to characters, plot, story-structure and language, then the agent would be considered a “creative literary agent”. The artifact, while anchored to the theme, would exhibit sufficient expansion (e.g., creative distance) relative to the input.
Any creative agent must begin with a seed of interestingness and maintain that theme in the generation of skillful variations that are sufficiently distinct from the input. Hitherto the invention, there has been no system which performed the above operations and in which interestingness and creative distance were even considered.
Thus, the conventional story generation system has been deficient in a number of areas as briefly discussed above.
Regarding knowledge sources, the conventional story generation system and method have used such sources only individually, in an unintegrated fashion. There has been no integrated, composite approach to story generation.
That is, the conventional methods have demonstrated analogs of lexical knowledge, in the form of natural language lexicons and grammars. Further, compositional knowledge, in the form of story grammars have been demonstrated. Additionally, domain knowledge, represented in many different ways from the logically formal (e.g., such as (∀(X) isa(X, man)→isa(X, mortal)) ^isa(socrates, man)isa(socrates,mortal) to the ad hoc (e.g., such as “socrates was mortal becomes he was a man.”).
However, these knowledge sources have neither been integrated and related to cooperative roles of an overall system architecture for story generation, nor of a more general architecture for computational creativity.
Regarding processes, the conventional story generation system and method has demonstrated process analogs for stage evolution, typically using planning and simulation techniques to generate story plots. Additionally, process analogs for structural expansion, typically using generative story grammars have been developed. Lastly, natural language generation has been developed.
Regarding system architectures, two basic architectures have emerged in story generation. The first architecture is based principally on plot development, whereas the second architecture is based on structural expansion. Both include some form of natural language generation.
An example of plot development through planning and simulation would be to provide a planning engine in which would implicitly ensure that the plot involved a character trying to achieve some goal. However, a problem is that sometimes a character's striving for a goal is tedious.
Thus, this approach to story generation is dominated by the process of plot expansion to produce variability in stories. However, the results have lacked a thematic anchor, and therefore struggled with the notion of “interestingness”. They also had no explicit knowledge component or mechanism for achieving impressionism (or in the special case of story generation, rhetoric). Story structure is part of an explicit architecture, and thus cannot be easily expressed or varied.
Further drawbacks include no representation or process for producing interestingness, no representation or process for structure expansion, and no representation or process of identifying and exploiting impressionistic knowledge. Impressionistic knowledge captures how an artifact might impact or impress human sensibilities through style and form, rather than explicit content. Knowledge about how the sounds of certain words affect the reader is considered “impressionistic knowledge”. Another example is knowledge about how different words, even though they may refer to the same thing, would produce different emotional states in the reader. Describing weapons of destruction as “ordnance” produces a different impression in the reader than describing them as “harbingers of death”. This class of knowledge may be acquired through the practice and study of literature and rhetoric and/or may be acquired and inferred statistically.
Another conventional implementation approach is structural expansion through story grammars in which through iterative structural expansion of a story grammar provides and builds increasingly detailed outlines. However, in this approach it becomes extremely difficult to represent the complex plot and literary variations in the declarative forms required by story grammars.
Thus, this approach to story generation focused on generative grammars and ignored the potential to achieve creativity and variability through plot expansion.
Thus, hitherto the present invention, there has been no system and method adequate for story generation in which a creative agent could begin with a seed of “interestingness” and maintain that theme in the generation of skillful variations that are sufficiently distinct from the input.